Bibliographic record
Abstract
Several years ago I was invited to do a reading tour of the Yukon, one of Canada’s territories in the far north. On one of the days I was flown in a little plane over the frozen white hills and the herds of migrating caribou, until we landed on a little gravel strip in the middle of nowhere where a fellow in a pickup truck was waiting for me. He drove me still farther north up winding gravel roads, where the snow got deeper and the trees got smaller, telling me the type of scary story that northerners like to tell southerners when they get them trapped. ‘Right over there is where old John Smith froze to death last week, sitting in the ditch with a beer bottle in his hand.’ As we got closer to the little mining village he said, ‘I don’t want you to be disappointed if not too many people come to your reading. It’s the middle of the day and all the miners are down the mine. But I can promise you one person in your audience. It’s in the library and I told the librarian she HAD to stay!’ Well, when we got to the library, not only had the librarian not stayed, she’d locked up the library and disappeared…
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.026 | 0.025 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".